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  1. Don't trust Fodor's guide in Monte Carlo: Learning concepts by hypothesis testing without circularity.Michael Deigan - 2023 - Mind and Language 38 (2):355-373.
    Fodor argued that learning a concept by hypothesis testing would involve an impossible circularity. I show that Fodor's argument implicitly relies on the assumption that actually φ-ing entails an ability to φ. But this assumption is false in cases of φ-ing by luck, and just such luck is involved in testing hypotheses with the kinds of generative random sampling methods that many cognitive scientists take our minds to use. Concepts thus can be learned by hypothesis testing without circularity, and it (...)
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  • Your visual system provides all the information you need to make moral judgments about generic visual events.Julian De Freitas & George A. Alvarez - 2018 - Cognition 178 (C):133-146.
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  • Causal Structure Learning in Continuous Systems.Zachary J. Davis, Neil R. Bramley & Bob Rehder - 2020 - Frontiers in Psychology 11.
    Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...)
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  • Analyzing Machine‐Learned Representations: A Natural Language Case Study.Ishita Dasgupta, Demi Guo, Samuel J. Gershman & Noah D. Goodman - 2020 - Cognitive Science 44 (12):e12925.
    As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations (...)
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  • Building Thinking Machines by Solving Animal Cognition Tasks.Matthew Crosby - 2020 - Minds and Machines 30 (4):589-615.
    In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard and its reliance on language, whilst insightful for partially solving the problem, has put AI progress on the wrong foot, prescribing (...)
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  • Editors’ Review and Introduction: Levels of Explanation in Cognitive Science: From Molecules to Culture.Matteo Colombo & Markus Knauff - 2020 - Topics in Cognitive Science 12 (4):1224-1240.
    Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at one level inform or constrain explanations at (...)
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  • Andy Clark, Surfing Uncertainty: Prediction, Action, and the Embodied Mind: New York: Oxford University Press, 2016, xviii + 401, $29.95, ISBN 9780190217013.Matteo Colombo - 2017 - Minds and Machines 27 (2):381-385.
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  • Intelligent Behaviour.Dimitri Coelho Mollo - 2022 - Erkenntnis 89 (2):705-721.
    The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the related concepts of cognition and (...)
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  • Digital life, a theory of minds, and mapping human and machine cultural universals.Kevin B. Clark - 2020 - Behavioral and Brain Sciences 43:e98.
    Emerging cybertechnologies, such as social digibots, bend epistemological conventions of life and culture already complicated by human and animal relationships. Virtually-augmented niches of machines and organic life promise new free-energy-governed selection of intelligent digital life. These provocative eco-evolutionary contexts demand a theory of (natural and artificial) minds to characterize and validate the immersive social phenomena universally-shaping cultural affordances.
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  • Empiricism in the foundations of cognition.Timothy Childers, Juraj Hvorecký & Ondrej Majer - 2023 - AI and Society 38 (1):67-87.
    This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from (...)
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  • Event‐Predictive Cognition: A Root for Conceptual Human Thought.Martin V. Butz, Asya Achimova, David Bilkey & Alistair Knott - 2021 - Topics in Cognitive Science 13 (1):10-24.
    Butz, Achimova, Bilkey, and Knott provide a topic overview and discuss whether the special issue contributions may imply that event‐predictive abilities constitute a root for conceptual human thought, because they enable complex, mutually beneficial, but also intricately competitive, social interactions and language communication.
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  • Event‐Predictive Cognition: A Root for Conceptual Human Thought.Martin V. Butz, Asya Achimova, David Bilkey & Alistair Knott - 2021 - Topics in Cognitive Science 13 (1):10-24.
    Butz, Achimova, Bilkey, and Knott provide a topic overview and discuss whether the special issue contributions may imply that event‐predictive abilities constitute a root for conceptual human thought, because they enable complex, mutually beneficial, but also intricately competitive, social interactions and language communication.
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  • Cognitive Models Are Distinguished by Content, Not Format.Patrick Butlin - 2021 - Philosophy of Science 88 (1):83-102.
    Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation....
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  • Bayes, predictive processing, and the cognitive architecture of motor control.Daniel C. Burnston - 2021 - Consciousness and Cognition 96 (C):103218.
    Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...)
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  • Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  • Deep learning: A philosophical introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10):e12625.
    Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...)
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  • How Is Perception Tractable?Tyler Brooke-Wilson - 2023 - Philosophical Review 132 (2):239-292.
    Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception’s computational tractability. One of the few attempts to move toward such an explanation is the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by forgoing (...)
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  • Explanation impacts hypothesis generation, but not evaluation, during learning.Erik Brockbank & Caren M. Walker - 2022 - Cognition 225 (C):105100.
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  • Recognizing why vision is inferential.J. Brendan Ritchie - 2022 - Synthese 200 (1):1-27.
    A theoretical pillars of vision science in the information-processing tradition is that perception involves unconscious inference. The classic support for this claim is that, since retinal inputs underdetermine their distal causes, visual perception must be the conclusion of a process that starts with premises representing both the sensory input and previous knowledge about the visible world. Focus on this “argument from underdetermination” gives the impression that, if it fails, there is little reason to think that visual processing involves unconscious inference. (...)
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  • Active inductive inference in children and adults: A constructivist perspective.Neil R. Bramley & Fei Xu - 2023 - Cognition 238 (C):105471.
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  • Sticking to the Evidence? A Behavioral and Computational Case Study of Micro‐Theory Change in the Domain of Magnetism.Elizabeth Bonawitz, Tomer D. Ullman, Sophie Bridgers, Alison Gopnik & Joshua B. Tenenbaum - 2019 - Cognitive Science 43 (8):e12765.
    Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...)
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  • Beyond Neural Coding? Lessons from Perceptual Control Theory.Xerxes D. Arsiwalla, Ruben Moreno Bote & Paul Verschure - 2019 - Behavioral and Brain Sciences 42.
    Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.
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  • Building machines that learn and think about morality.Christopher Burr & Geoff Keeling - 2018 - In Christopher Burr & Geoff Keeling (eds.), Proceedings of the Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2018). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
    Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...)
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Linguistic Competence and New Empiricism in Philosophy and Science.Vanja Subotić - 2023 - Dissertation, University of Belgrade
    The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...)
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  • Can resources save rationality? ‘Anti-Bayesian’ updating in cognition and perception.Eric Mandelbaum, Isabel Won, Steven Gross & Chaz Firestone - 2020 - Behavioral and Brain Sciences 143:e16.
    Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?
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  • Indicators and Criteria of Consciousness in Animals and Intelligent Machines : An Inside-Out Approach.Cyriel Pennartz, Michele Farisco & Kathinka Evers - 2019 - Frontiers in Systems Neuroscience 13.
    In today’s society, it becomes increasingly important to assess which non-human and non-verbal beings possess consciousness. This review article aims to delineate criteria for consciousness especially in animals, while also taking into account intelligent artifacts. First, we circumscribe what we mean with “consciousness” and describe key features of subjective experience: qualitative richness, situatedness, intentionality and interpretation, integration and the combination of dynamic and stabilizing properties. We argue that consciousness has a biological function, which is to present the subject with a (...)
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  • How feasible is the rapid development of artificial superintelligence?Kaj Sotala - 2017 - Physica Scripta 11 (92).
    What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and (...)
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